Sample Size Calculator
Determine how many responses you need for your survey or experiment. This tool supports: proportion-based surveys (the classic approach), mean/continuous outcomes, and finite population correction when your audience is not huge.
choose the confidence for your estimates
0.05 → ±5 percentage points
0.5 → most conservative
apply FPC if filled
use pilot estimate
Cochran's formula is a classic way to get an initial sample size for large populations: \( n_0 = \frac{z^2 p(1-p)}{e^2} \). Then, if your population is small, adjust with FPC: \( n = \frac{n_0}{1 + \frac{n_0-1}{N}} \).
Initial sample size (n₀)
—
ignoring finite population
Adjusted sample size (n)
—
with FPC if N provided
z-score used
—
Sample size formulas
Survey / proportion:
\( n_0 = \frac{z^2 \, p (1 - p)}{E^2} \)
FPC: \( n = \frac{n_0}{1 + \frac{n_0 - 1}{N}} \)
Mean / continuous:
\( n_0 = \left( \frac{z \sigma}{E} \right)^2 \)
What should I enter as p?
If you do not have a previous study or pilot, use 0.5. That produces the largest required sample size and is safe.
Quick reference
| Confidence | z-score | Typical MoE |
|---|---|---|
| 90% | 1.645 | ±5% |
| 95% | 1.96 | ±5% |
| 99% | 2.576 | ±3% |
Formula (LaTeX) + variables + units
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Survey / proportion: \( n_0 = \frac{z^2 \, p (1 - p)}{E^2} \) FPC: \( n = \frac{n_0}{1 + \frac{n_0 - 1}{N}} \) Mean / continuous: \( n_0 = \left( \frac{z \sigma}{E} \right)^2 \)
- No variables provided in audit spec.
- NIST — Weights and measures — nist.gov · Accessed 2026-01-19
https://www.nist.gov/pml/weights-and-measures - FTC — Consumer advice — consumer.ftc.gov · Accessed 2026-01-19
https://consumer.ftc.gov/
Last code update: 2026-01-19
- Initial audit spec draft generated from HTML extraction (review required).
- Verify formulas match the calculator engine and convert any text-only formulas to LaTeX.
- Confirm sources are authoritative and relevant to the calculator methodology.